\chapter{Pilot Study: Music Recommendations for Events on Social Networks}
\label{chapter4}
%\begin{quote}``Imagination is more important than knowledge"\end{quote}\begin{flushright}Albert Einstein\end{flushright}
In order to model the intentions of the user. The virtual actions are important to monitor as discussed in \ref{intent}. In this chapter, a music recommendation application developed for this purpose is presented. The application is based on a Social Networking Website called Facebook\footnote{\url{http://www.facebook.com/}}. The methodology is described in detail and results of the experiment shall be presented with the help of statistical evaluation of experiment which would show the significance of this application for music recommendation. 

\section{Abstract}

Purpose - To explore the roles social networking applications play in music diffusion.

Design/methodology/approach - To examine the paths of influence of online social networks on fans of music and to validate the intermediating roles of the cognitive variables.

Findings - The results show that the three factors associated with the innovation diffusion theory play different intermediating roles in the relationship between the stickiness of online social networks and the levels of passion displayed by fans of music. 
%While stickiness shows a significant positive impact on an individual’s perception and image of the band, visibility has a significantly negative influence on the individual’s level of fondness of the targeted rock music band.

Research limitations/implications - Additional data and measures are required for in-depth investigations of other cultural contexts.

Practical implications - The study could be helpful for determining the influence of online social networks on music diffusion and for planning innovative promotions and sales strategies for music. Moreover, the findings can serve as references for the marketing and promotion of other culture items.

Originality/value - To confirm the intermediating roles of the cognitive variables based on innovation diffusion theory between online social networks and music fondness by using a mixed qualitative and quantitative approach and then by showing the opportunities and challenges provided by social networking to music.

\section{Introduction}
The exponential growth of the social web continues to pose challenges and opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.\\
Music is a part of our everyday life. The most popular interest keyword according to the year 2009 statistics on Online Social Network profiles was `Music' \cite{5231898}. There are several factors influencing our music taste including culture, circumstances, social network, etc. Many efforts to integrate this human nature of `being a part of community' in music recommender systems are on-going. A most recent example of this is the Spotify integration with Facebook. The friends music taste was initially revealed as a trend of friends' music preferences but after several complaints, this service was revoked within a week of its launch on Facebook. That said, it is an alarming indicator for the efforts towards the transparency of music recommendations. 
One of such approaches is collaborative filtering on music listening history of users within Web Music Community such as Last.fm\footnote{\url{http://www.last.fm/}}. The notion of ``Music Feed" has also emerged. Pandora has recently attempted to add the feature of Music Feed on Pandora One, which shows the activities of friends such as likes, tracks friends are listening to and associated comments. It is a similar concept to Ping but is only available to the paid subscribers and is currently in testing phase \cite{pandora}. It is observed that “music discoveries are often result of passive behaviour” and that seeking for music is a “highly social activity”\cite{Laplante08}. It could be inferred from this information that there is a much greater chance of people not actively looking for discovering music on the WMC. Whereas, the popular Online Social Networks (ONSs) such as Facebook is more likely to be used more frequently by the users to keep in touch with the family and friends. The number of active users on Facebook\footnote{\url{http://www.facebook.com/press/info.php?statistics}} outnumbers any of the WMCs by a significant margin. Therefore, it is more likely to find real life friends on Facebook as compared to the WMCs, forming another motivation to conduct our experiment on Facebook.\\ Since, music preference is influenced by many factors such as social and cultural background as well as emotional state of the user. It is sensible to collect information regarding the music preferrences  of user's social contacts in order to generate recommendations according to user's with her socially influenced music taste. The application of social media to the collection, storage and review of social network of the user presents opportunities for improved social music recommendation systems. A novel approach to music recommendations for events through social media is proposed in this work. People post event (such as party, wedding, etc.) information on Social Networking Services. Selection of music to be played at such events is usually not an easy task. In order to facilitate the event organiser with music selection, a music recommendation application called `Music Valley' is developed on Facebook, which generates recommendations based on the music preferences of the people attending the event. This experiment shall form the foundation for the framework of a better Music Recommendation Systems combined with social media. This framework shall then be used to develop a prototype for the system design.  According to Facebook Statistics\textsuperscript{3}: ``Average user is connected to 80 community pages, groups and events". The concept of profiles, groups, pages, and events (in the context of Facebook) is further explained in the following sections. 

\section{Facebook API}
The Facebook API is a platform for building applications that are available to the members of the social network of Facebook. The API allows applications to use the social connections and profile information to make applications more involving, and to publish activities to the news feed and profile pages of Facebook, subject to individual users privacy settings. With the API, users can add social context to their applications by utilizing profile, friend, page, group, photo and event data. The API uses RESTful protocol and responses are localized and in XML format.

\subsection{Profiles, Pages and Groups}
The users have profiles on Facebook with a quick summary of one's interests, education, music preferences, and connect with other profiles of people they know or share interest with\footnote{\url{http://www.facebook.com/about/profile/}}. Unlike MySpace, the artists on Facebook have the same profile structures as normal users. Therefore, if an artist uses our application, it behaves in the same manner as in case of a normal user.\\ The limit on the number of friends a profile has forced artists to create pages\footnote{\url{http://www.facebook.com/pages/browser.php}}, which are more open and better way for publicity and connecting to thousands of fans online. The fan pages created can be`liked' to receive updates in the News Feed. Facebook is now trying to interlink the pages with user profiles. In the music section of my profile, e.g. if I listed Madonna as my music preference than the fan page of Madonna is automatically linked to my profile and I can see news feed from that page. The pages in music category can lie in any of the following subcategories:

\begin{itemize}
\item Musician/Band
\item Concert Venue
\item Concert Tour
\item Music Theater
\item Musical Intrument 
\item Music Chart
\item Music Award
\item Playlist
\item Music Video
\item Record Label
\item Radio Station 
\item Song
\end{itemize}

People who liked the page can like and comment on its content i.e., if the page owner has granted the permission for this. Another solution for people with shared interests (such as common music taste) can form a Facebook Group. Users can join and create up to 200 groups. Groups can be based around shared interests, activities, etc. Groups can be accessed via its application page\footnote{\url{http://www.facebook.com/apps/application.php?id=2361831622/}} or default tab on the profile page. It displays the recently updated groups as well as groups that user's friends have joined. Questions\footnote{\url{http://www.facebook.com/questions/}} lets you learn about the likes, interests, and recommendations of your friends and others by asking questions. These questions can be posted in user's own network, joined groups and pages Also, Event is a very attractive application which is discussed below.

\subsection{Events}
\label{event}
Event application\footnote{\url{http://www.facebook.com/apps/application.php?id=2344061033/}} allows users to post an event on Facebook. It can be created via user profile page or in a fan page or in a group. With Facebook Events, user can organize gatherings and parties with friends, as well as let people in the network know about upcoming events. The event is posted on Facebook by the event organiser or someone else on her behalf, it can be public or private event. If the event privacy is set to private then only the invited guests can see the event. If the event is public anyone from user's friends or friends of friends can join the event even if they were not invited explicitly. The event creator/organiser then invites her friends to the event or share it in a group and share on user's or friend's wall. The invited friends have three options to choose from; the options are attending, not attending and might be attending. They also have the option to write RSVP note which is optional.The Events page displays the upcoming events, any pending invitations, and links to user's own and friends events. Recently, a new feature is added to event page that has `Birthday' link which displays upcoming friends' birthday notifications in a user's network, ordered by months.\\ Unfortunately, Event page does not provide categorisation of events i.e. there are no event pre-defined types specified while creating an event in facebook. The events are usually described using a list of words such as “marriage ceremony”, “birthday party”, etc. Therefore, there is a need of word-sense disambiguation for categorisation of events. However, events are classified in various categories using keyword based approach on a website called Facebookallevents\footnote{\url{http://www.facebookallevents.net/}}. It classifies public events into various categories such as by location (city), by event type (category). Event interlinking can be enhanced if we can get the publically checked in people profile via check-in feature\footnote{\url{http://www.facebook.com/places}} on Facebook.

\section{Significance of the Experiment}
\begin{quote}``The music industry used to be a service then it became a product and now seems its becoming a service again" Jay Z\end{quote}
The web services and music applications for organising the events are artist-centric or location-specific such as Songkick\footnote{\url{http://www.songkick.com/}}. Concert-listings site Songkick recently released an Application. Once installed, it scans the music library of user and synchronises the favorited artists with those that are touring in near the user's location, creating a calendar of upcoming shows. It has the feature to instantly track the favorite artists from users profile on Web Music Communities such as Pandora, Last.fm and iTunes, when granted permission by the user. The concerts are categorised according to the artist and/or location. While tracking concerts is a relatively a music enthusiasts' activity, it is a behavior that could translate over to the mainstream market. Youtube has also integrated SongKick's concert-listing feature in its Music section.\\ %In the first two weeks, the app surpassed 100,000 downloads. 
There are many events in our life such as birthday party, friends/alumni get-together party, family reunion, wedding, etc. where music is played but it is usually not a live concert rather different recorded songs by different artists are played on music system. In such events, many people are invited and the music taste of the attendees may vary greatly. Therefore, if we incline towards single artist and/or genre only, then there are chances that all of the attendees of the event might not enjoy the music played very much. This experiment is designed to help event organisers to select music for such events based on the preferences of the attendees of the event. The attendees are provided with options to influence the music to be played on the event that they are going to attend. The hypothesis for the current pilot project is as follows:

\subsection{Hypothesis}
\begin{quote}
``User profiles on social networking websites can be a good source of information to generate successful music recommendations to create a more intention-aware system regarding music."
\end{quote}

When the event attendees music taste is extracted from their profiles, the chances of music discovery also increase. As the event organiser might have never listened to the genre people in his/her social networks have been listening to. With this experiment we can analyse the response of people to recommendations. The recommendations are randomly picked from the pool of music preferences of oneself, and other people. Each time a track is recommended, the subject of the experiment rates the track either post it to her wall if she likes the recommendation or uses the shuffles feature to see more recommendations. It seems that if we are successful in modeling the intentions of users for selection of songs at particular events, it can be very useful to suggest songs to other users organising similar events.\\
Temporal analysis of music listening behaviour was observed by \cite{5591002}. Results indicate that some genres are preferred at specific time of day, on particular seasons or months. Similarly, if we try to model preferences of consumers on particular events, it shall form another step towards contextual awareness.

\subsection{Music Valley Application}
The idea behind the Music Valley application is to facilitate the event organiser in music selection for the event. The event can be posted in the usual manner using the ‘create an event’ option built-in Events feature within the users profile, group and page. It may involve music such as party, wedding, etc. The problem of selecting music to be played for a range of people attending an event is approached by automatically extracting information regarding music preferences by the attendees of the event using their profile page. Only invitees who marked themselves as `attending' are of interest here as they will potentially attend the event. The suggested playlist recommendations are based on the music preferences extracted from the user profiles marked as attendees of the event. Thus, event organiser can easily make decisions on songs selection by listening to the popular and suggested song videos fetched from Youtube. The event organiser and attendees can see the music preferences of their friends, or people outside of user network who have set their likes (which includes music preferences) privacy setting as public. The music can therefore, be recommended according to their music taste as specified in their profiles. As dicussed in section\ref{event} event categories have not yet been defined. Therefore, the event type will not be playing any role in music selection by this app. However, if in near future that happens, determination of suitability of music within a particular event category requires more sophisticated algorithms than mere collaborative filtering. The attempts to post the recommendations to the event wall have failed due to spamming issue\footnote{\url{http://www.simplysecurity.com/2011/04/04/facebook-users-get-invited-to-a-spam-event/}} which arose in April 2011. The users were invited to spam events. Due to which programmatically the link posting was banned. So, we could not post links to the Event Wall. Hence, a compromise was made and option to post links to the user wall was provided. We believe that analysis of music taste of the attendees of the events can enable the event organiser to make better decisions on what type of music shall be enjoyable for most of the attendees of the event.\\
Social music recommendations enable people to discover music through a serendipitous process powered by human relationships and tastes, exploiting the user's social network to share cultural experiences. The successful music recommendations shall be indicated by the user of the application which will strengthen the belief that music preferences listed in the attendees’ profiles indicate their real taste for music. Youtube\footnote{\url{http://www.youtube.com/}} has been used to make random recommendations based on music preferences listed in the users profiles. The reason for selecting Youtube is that it is preffered by users to have a feel of atmosphere; usually because it has video accompanied with the song.\\ 

\section{Design of the Study}

\subsection{Methodology}
An experiment was setup to test the hypothesis stated above. The recommendation problem is defined as given a pool of items, the subject selects an item that is relevant to her. The results from this approach result from the breadth first search. The speed and the amount of memory used by this searching algorithm is most productive in terms of this application.\\
The application to extracts music preferences of the top 40 friends attending the event. The reason for this limit on number of attendees is that the attendees' preferences are extracted from Facebook in real time and not from a database, therefore, the server time limit to load a page hinders the extraction of all attendees which could be in thousands, in some cases.
%A collaborative recommendation makes use of previously rated items by other subjects to choose the item to be recommended. If collaborative recommendations issued to a subject on items rated by people from the social network of the subject lead to more successful recommendations than on items rated by people not in the subjects social network, then we show that social recommendations are more appropriate as collaborative recommendations. If this is verified then we can conclude that discoveries are diffused through the social network.
Currently, the web  user interface design is modest. It is user-centered design and focuses on user needs/tasks.

\paragraph{Dataset}
{The Youtube recommendations are used as the dataset which serves as pool of tracks for the recommender. The artists/musical bands/genres preffered by the users are randomly selected among the music preferences of the attendees and then YouTube API is used to search for top most songs for this artist/musical band/genre. People currently use Youtube music videos to share music on the internet, by sharing links by email or within social networks. The recommended tracks that user shares are recorded in the database for analysing success of recommendations.}

\paragraph{Facebook Application}{Music Valley\footnote{\url{http://apps.facebook.com/music_valley}} is a Facebook application fed by ratings on random selections. A positive rating spawns diffusion through the user's social network, ie the shuffle recommendation becomes social.  An initial sketch of the Graph of the social network of the 10 participants is shown in \ref{fig:Graph}.
%The interface (Figure \ref{fig:Graph}) consists of the current track description (its title and artist name), the music video associated, a tag cloud of the user's profile, a rating form consisting of 5 stars and a "Next" and Bail button(sets the stars to 0 and votes).}

\begin{figure}[h]
\begin{center}
\includegraphics[width=.7\textwidth]{Figures/Graph}
\caption{\label{fig:Graph}Connected Graph on Facebook.}
\end{center}
\end{figure}

\paragraph{Subjects}{Each subject acts as her own control group by getting random recommendations based on music preferences of the users attending the event. The subjects are originally from my own social network and was extended as people were then invited by the ones who joined the application. The experiment currently has 24 unique users out of which around 34 ratings have been made by 3 users (excluding myself) from August 1, 2011 till today.

\subsection{Recommender Model}
As seen in \ref{recommender} sections, users and/or items remain the central focus in a recommender systems. Modelling users is essential in order to use their profiles and preferences adequately. An adaptive recommender system seeks to adapt to its user to provide personalised services\cite{adaptive}. The recommender systems should accurately describe the items that meet user expectations \cite{OC10}. Therefore, the list of artists alongwith the metadata and name of the friend who likes them, seems to fulfill the user expectations. The user feedback plays essential role in learning user behaviour and adapting the system to her/his needs. The user is therefore, provided with the link to give feedback. A mix of pre-defined essential questions and comment section for free text is provided. Different use cases are discussd for further ellaboration of the recommender model. 

\subsection{Recommendation strategy}
The goal of the recommender system is to make the best recommendation possible and produce a setting where we can test the hypothesis. A better recmmendation system would make use of the genres (or tags) of the tracks previously rated by the subject. Our recommendation system simple uses a random function to populate the song playlist. The random selection is a query which selects the tracks that have not been rated by the subject and orders them by random numbers.}

\subsection{Use Cases}
Music Recommender System(MRS) is supposed to propose music that interests the user. It could be from known or unknown artists. Mostly, the MRS generates a personalised playlist or a list of artists\cite{OC10}. 

\subsubsection{Artist Recommendation}
In this case, the artists are recommended based on music preferences of user profiles.
% user-item matching model based
The produced list of attendees is interactive. The music preferences can contain artist name along with some metadata. A broader experience than that for the user is expected to be more appreciated. Therefore, MRS to incorporate post on user wall feature, which shall appear in News Feed of the user and her network. Her friends can then comment or like the recommendation or visit the Music Valley Application to create a recommendation of their own. The wall post also include link to event and playlist.
%Really Simple Syndication(RSS) for recommending related news, music releases, concerts, etc. Publishing services such as iTunes Music Store's RSS Feed Generator\footnote{\url{http://ax.itune.apple.com/rss/}} can be utilised for this purpose. It publishes all the new releases, updated once a week.

 \subsubsection{Playlist Generation}
Playlist generation is important application of Music Recommender Systems. It allows user to listen and provide feedback immediately and the system adapts accordingly. User's listening history and feedback on these lists help system to learn user behaviour and adapt itself accordingly as analysed by \cite{Cunningham06}The shuffle feature is provided to automatically create a playlist based on given seed song/artist/user profile including her like-minded neighbours as well. The main modes of playlist generation is tracks suggested for users' personal selection or randomly selected from list of preffered artists. 
%Tracks from celestial jukebox such as tracks collected from music content resolver e.g. Playdar\footnote{\url{http://www.playdar.com}}
Research has shown that music listeners sometimes enjoy randomly ordered recommendations \cite{leong05}. The serendipity associated with the random playlist can provide opportunities for discoveries, it can give new meaning to a particular track and connect songs to old memories. Arguably, it is possible that the serendipity process might be fostered with more personalised and elaborated playlists as compared to pure random ones.\\ 
Usually, a playlist is for personal use and order of songs is not very important \cite{Cunningham06}. Personalised playlists also known as a Radio-a-la-carte is another way to propose music to a user. Music is selected in terms of the user preferences within a particular context. It is compiled based on user taste and actual listening context. It focuses on desired emotional state or act as background activity such as listening to a playlist while working, jogging, reading, etc. \cite{OC10}. However, in our case it is not only for personal use. Its main purpose is to recommend music for events.
%The user can also provide feedback such as skipping the song, similar to this one, etc
\subsubsection{Neighbour Recommendation}
As the name of this type of recommendation signifies, it tries to connect user profile with similar taste profiles in a community. Neighbour similarity is based on user-user profile matching. Finding relevant profiles provides greater chances of music discovery and exploration. This feature can be added at a later stage.

\subsubsection{Other Recommendations}
The idea to make use of connected graphs in social networks to recommend music is interesting. The concept opens an exciting field of investigation for research in MIR with space for improvement and further research for the community. One related work shall be presented in WOMRAD 2011 by myself. There are other interesting scenerios such as generating recommendations for a group of users in a particular context \cite{OC10}. It could be in the form of automatic music selection in a gathering such as a party to please as many people as possible on that occasion. In places like restaurants, proposing background music can be selected by using MRS with some constraints such as relaxing, ambient, intrumental, etc. going to the extent such as restricting only Punjabi Folk songs in typical Punjabi restaurants or Italian songs in Pizzeria. 

%\paragraph{Social}{the social recommender selects a track randomly from the tracks that have been rated by friends of the subject with a rating superior to 2 stars.}
%\paragraph{Non-social}{the non-social recommender selects a track randomly from the tracks that have been rated by people who are not friends of the subject with a rating superior to 2 stars.}
%Actually this is equivalent to simply make a collaborative recommendation which might be social or non-social. We can test later on, at the time of the analysis of the results if the recommendation was social or not by looking if a friend of the subject made a high rating prior to the recommendation or not.
%\paragraph{Collaborative}{selects a track randomly from the set of tracks that have been rated with a rating above 2 stars and not yet rated by the subject.}

%\begin{verbatim}
% 1 . r1 = rand(0,1)
% 2 . if r1 < alpha then
% 3 .   track = rand_track
% 4 . else
% 5 .   r2 = rand(0,1)
% 6 .   if r2 < beta
% 7 .     track = social_track
% 8 .   else
% 9 .     track = non_social_track
% 10 . return track
%\end{verbatim}

%\subsection{Statistical method}
%To conduct a statistical analysis of the subjects ratings, I generated histograms of the relative frequencies in percentage for various interests (number of stars per ratings). We look here at the various measurements and methods.

\subsection{User Feedback} 
The feedback form provided is very useful so far. The users have provided constructive feedback regarding various issues. It is very encouraging to see that it is not mere criticism rather these are suggestions for improvements. The advantages of using the application as indicated by the users are as follows:
\begin{itemize}
\item The user was surprised to see her favorite songs being recommended to her, I ellaborated the experiment and she told me that the bands/artists she listed in her preferences (a total of 8) were \emph{not} the bands she actually listened to, rather they were some friends whom she was asked to promote by `liking' the page. This revealed to me that the recommendations generated from friends' preferences were leading to more useful results for this user.
\item Saves time for a user to think of what she wants to listen next (as the songs are recommended as a playlist).
\item The system has been successful in re-discoveries of songs by musician/bands that a user admired while she was a teen but had forgotten about.
\item The usual Youtube recommendations did not interest a user as she said: ``I never like the recommendations - it is always what Youtube want to sell me i.e. the songs by the song owners / publishers - who always have advertisements at the start of the clip. So, I never click on these recommendations but this is much better as a player because its playing the songs I know, and want to listen to and not the ones that will show ads first".
\item Sarcastic appreciation: ``This system is creepy! I have been recommended a song from a band that I used to login to Facebook account!! I smell conspiracy theory here... ".
\end{itemize}
The users' suggestions to improve the quality of recommendations are as follows:
\begin{itemize}
\item The application asks for many permissions; an explanation before a user is asked to grant the permissions would be helpful to know what is going on...
\item Another comment: ``The next stage for music recommendation people could also pick the quality they like to listen to i.e., only choose clips that have HD sound or don't pick the ones that are kids playing in their bedroom!".
\item The songs should be mixed within the playlist from different artists, it makes it boring to use the system for a randomly picked artist playlist.
\end{itemize}
Unfortunately, the comments and suggestions seem to be more on personal use of playlist. The event organisers/attendees did not comment how well the music recommendations were for the event perspective and if they had been used to arrange a playlist for any event. Therefore, I have added some pre-defined essential questions along with the comment section for free text. The reason for essential questions is to check whether the system is meeting basic criteria of usability. 

The Adobe® Flash® Platform is an integrated set of application programming technologies surrounded by an established ecosystem of support programs, business partners, and enthusiastic user communities. Together, they provide everything you need to create and deliver compelling applications, content, and video to the widest possible audience across screens and devices.
\section{Problems Faced during Application Development}
In this section, I intend to list the problems that I faced during the development on Social Platform and some internal problems in University server.\\
In March, I had started to read a book \cite{Maver} for Facebook Application Development. It mainly focused on Facebook Markup Language (FBML) for Facebook Application Development. Unfortunately, all the efforts to start development using FBML failed, as in the end of March (without any prior notice), FBML was announced to be deprecated on Facebook Platform. Then, I started using alternative approaches. I used HTML, PhP and JSON. While developing the application, many issues arised. Firstly, the host location provided by the University\footnote{\url{http://webprojects.eecs.qmul.ac.uk/ar304}}, this web server had PhP version 5.1 installed, which is quite outdated. I needed cURL extension which was supported in PhP versions greater than 5.3.7, many other similar things for using the Facebook API. It is expected to be upgraded around October this year. Therefore, I had to look for other options and I asked for a place on Isophonic server which was also not supporting higher versions of PhP. So, some complicated installations made it a bit hard for me to understand the behaviour of this web server. But after long effort, in the end of June, my server issues were finally resolved. Meanwhile, I was developing my application and testing on the local server, i.e. WampServer. However, then there was more to come from Facebook end. Facebook application had some security flaw reported in May, 2010 by Semantyc \cite{privacy} which indicated that the user information might have been compromised by the FB apps. This Facebook privacy breach might have leaked users' information to Advertising and tracking firms. The security patch applied in late June, stopped apps from using the Public data on which my App was heavily relying upon. I looked for alternative ways to resolve this issue. Then I used the user's own social network and her social network information which compensated this problem. Actually, this is the crucial part of the project. The users who are attending an event are the underlying motivation for the song suggestion. Since, the users profile on the FB displays the type of music the users like, one can populate the most popular songs sorted by the band/musician/genre mentioned in the user profiles. The interest on Music Genre (techno,pop, rock etc) is extracted from the users profiles. It seems very unfair the way Facebook changes, before there used to be tabs for builiding applications that allowed a very smooth transitions from one function to another, but in the end of July the tabs have been eliminated. Anyhow, I have redesigned the web application for which I now used Flash for making the user experience more interactive. The main reason for using Flash was that I could not use Zend Framework, as the PhP version on webprojects could not support it and on Isophonics as I have already mentioned due to complexity of web server, I had already faced several issues. Therefore, I used Flash for video streaming from Youtube. The Graph API provided by Facebook created some issues while connecting with the server and resulted in proxy errors. However, I have found that the time to retrieve the user queries has become much more efficient with designing the whole application in Flash. It has provided the flexibility with time to retrieve as well which was the main hindrance in launching the app. If a user was an attendee to an Event that had more than 9 attendees. Since, the data is fetched from Facebook databases, the $max_execution_time$ limit of 30 seconds was not enough. Therefore, Flash time limit of 115 seconds was much more flexible. Now, the application is much more stable even if the user is an attendee to an event that has more than thousands of attendees. Although, I have currently fixed the limit to fetch music preferences for upto 40 users.

%http://digitalzoomstudio.net/2011/08/01/reasons-why-flash-is-a-better-solution-for-streaming-video-on-the-internet-then-html5/
\section{Schedule} 
It was estimated to span over a period of 4 months before any appropriate conclusions could have been made available. Since, the application user base was very small i.e. 30 users in total. Therefore, the results of the experiment have only partially helped to strengthen the belief that the hypothesis is correct. 

\section{Conclusions}
There are many problems that need further investigation such as how this recommended system might be integrated with the existing systems for music listening, etc. 